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October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
October 7, 2026
‘Our Team Consists of True Leaders in Their Respective Academic Disciplines
The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.
October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.

 

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Тематические модели: добавление биграмм и учет сходства между униграммами и биграммами

Вычислительные методы и программирование. 2015. Т. 16. № 2. С. 215–234.
Nokel M., Loukachevitch N. V.

The results of experimental study of adding bigrams and taking account of the similarity between them and unigrams are discussed. A novel PLSA-SIM algorithm based on a modification of the original PLSA (Probabilistic Latent Semantic Analysis) algorithm is proposed. The proposed algorithm incorporates bigrams and takes into account the similarity between them and unigram components. Various word association measures are analyzed to integrate top-ranked bigrams into topic models. As target text collections, articles from various Russian electronic banking magazines, English parts of parallel corpora Europarl and JRC-Acquiz, and the English digital archive of research papers in computational linguistics (ACL Anthology) are chosen. The computational experiments show that there exists a subgroup of tested measures that produce top-ranked bigrams in such a way that their inclusion into the PLSA-SIM algorithm significantly improves the quality of topic models for all collections. A novel unsupervised iterative algorithm named PLSA-ITER is also proposed for adding the most relevant bigrams. The computational experiments show a further improvement in the quality of topic models compared to the PLSA algorithm.

Priority areas: IT and mathematics
Language: Russian
Text on another site
Keywords: perplexityTopic Modelsтематические моделиPLSAPLSAbigramstopic coherenceбиграммысогласованность темперплексияword association measuresассоциативные меры
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